I'm looking for good exopository books and resources for probability concentration inequalities that covers both the theoretical aspects and practical applications of these inequalities. My interest primarily lies in understanding how they can be applied to fields such as analysis, geometry, and random phenomena. These inequalities to be powerful tools in various contexts. For example, Chernoff bounds provide exponential decreasing bounds on tail distributions, which are crucial in random graph theory and algorithmic randomness. I'm looking for books that not only cover the fundamental theorems and proofs but also provide insights into the application of these inequalities across different mathematical and computational domains. Ideally, the resources would balance theoretical rigor with practical application scenarios, helping bridge the gap between abstract probability theory and tangible computational problems.
Could anyone recommend books or literature that delve deeply into the topic of probability concentration inequalities, with a good mix of theory and application examples in analysis, discrete geometry, and algorithms?
Your suggestions will be greatly appreciated and will significantly aid in my research journey.
Thank you in advance!